• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.194-197
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• 2016

자동차 산업이 발전하면서 안정적인 주행과 운전자의 편의성을 위한 지능형운전자보조시스템인 ADAS (Advanced Driver Assistance System)가 이슈가 되고 있다. 차선인식의 결과에 따라 차선이탈 경고시스템의 성능이 달라지기 때문에 차선인식은 ADAS에서 매우 중요한 핵심적인 기술이라 할 수 있다. 이에 본 논문에서는 그림자 영역과 같이 밝기의 분포가 균일하지 않는 환경에서 강인하게 동작하는 차선인식 알고리즘을 제안하였다, 지역적인 밝기 특징을 고려하여 차선에 해당하는 특징점을 추출하며, 추출된 특징점 가운데 이상치(outlier)를 제거하기 위해 RANSAC (RANdom SAmple Consensus) 알고리즘을 이용하여 차선을 검출한다. 또한 RANSAC 알고리즘에서 신뢰도가 높은 차선이 검출되면 그 주위에 특징점을 추출하기 위한 관심영역을 설정함으로써 안정적인 차선 검출이 가능하도록 하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.3
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• pp.64-73
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• 2011

This paper presents an algorithm for tracking lanes on the road based on corner detection techniques. The proposed algorithm shows high accuracy regardless of lane divider types, eg, solid line, dashed line, etc, and thus is of advantage to city streets and local roads where various types of lane dividers are used. A set of experiments was conducted on real roads with various types of lane dividers and results show an extract ratio over 87% in average.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.6
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• pp.15-24
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• 2011

As increase awareness of intelligent vehicle systems, many kinds of lane detection algorithm have been proposed. General boundary extraction method can bring good result in detection of lane on the road. But a shadow on the road, or other boundaries, such as horizontal lines can be detected. The method using morphological operations was used to extract information about Lane. By applying HSV color model for color information of lane, the candidate of the lane can be extracted. In this paper, the lane detection region was set by Hough transformation using the candidate of the lane. By extracting lane markings on the lane detection region, lane detection method can bring good result.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.3 s.33
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• pp.87-94
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• 2005

3D positional information of lane can be automatically calculated tv combining GPS data, IMU data if coordinates of lane centers are given. The Road Safety Survey and Analysis Vehicle(RoSSAV) is currently under development to analyze three dimensional safety and stability of roads. RoSSAV has GPS and IMU sensors to get positional information of the vehicle and two road-facing CCD cameras for extraction of lane coordinates. In this paper, we develop technology that automatically detects centers of lanes from the road-facing cameras of RoSSAV. The proposed algorithm defines line-support regions by grouping pixels with similar edge orientation and magnitude together and extracts a line from each line support region by planar fitting. Then if extracted lines and the region in-between satisfy the criteria of brightness and width, we decide this region as lane. The proposed algorithm was more precise and stable than the previously proposed algorithm based on brightness threshold method. Experiments with real road scenes confirmed that lane was effectively extracted by the proposed algorithm.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.35-46
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• 2009

Lane extraction and lane departure warning algorithms using the image sensor attached in the vehicle are addressed. With the research about intelligent automobile, there have been many algorithms about lane recognition and lane departure warning system. However, since these algorithms require to detect 2 lanes, the high time complexity and the low recognition rate under various driving circumstances are critical problems. In this paper, we present a lane departure warning algorithm using single lane extraction and center point analysis that achieves the fast processing time and high detection rate. From the geometry between camera and objects, the region of interest (ROI) is determined and splitted into two parts. Hough transform detects the part of the lane. After the detected lane is restored to have a pre-determined size, lane departure is estimated by calculating the distance from the center point. On real driving environments, the presented algorithm is compared with previous algorithms. Experiment results support that the presented algorithm is fast and accurate.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.797-807
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• 2008

This paper proposes a new algorithm of extracting forward vehicle areas using the acquired lanes and road area information on road images with complex background to improve the efficiency of the vehicle detection. In the first stage, lanes are detected by taking into account the connectivity among the edges which are determined from a method of chain code. Once the lanes proceeding to the same direction with the running vehicle are detected, neighborhood roadways are found from the width and vanishing point of the acquired roadway of the running vehicle. And finally, vehicle areas, where forward vehicles are located on the road area including the center and neighborhood roadways, are extracted. Therefore, the proposed scheme of extracting forward vehicle area improves the rate of vehicle detection on the road images with complex background, and is highly efficient because of detecting vehicles within the confines of the acquired vehicle area. The superiority of the proposed algorithm is verified from experiments of the vehicle detection on road images with complex background.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.427-429
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• 2017

본 논문에서는 실시간으로 주행 중인 차량의 영상을 대상으로 ROI 영역을 추출하고 추출된 ROI 영역에 Warping 기법과 RANSAC 알고리즘을 적용하여 곡선 차선을 검출하는 방법을 제안한다. 제안된 방법은 실시간 영상에서 관심 영역을 ROI 영역으로 설정하고 영상의 원근감을 제거하기 위하여 Warping을 적용한다. Warping이 적용된 영상에서 차선의 밝기는 도로의 밝기보다 높다는 특징을 이용하여 노란색과 흰색 차선의 영역을 추출한다. 추출된 차선의 영역에서 곡선을 검출하기 위하여 RANSAC 알고리즘을 적용하여 곡선을 검출하기 위한 기준점을 설정한 후, 스플라인 기법을 적용하여 곡선을 검출한다. 실시간적으로 주행 중인 차량에서 촬영한 동영상을 대상으로 실험한 결과, 곡선 차선이 효과적으로 검출되었다. 따라서 제안된 방법이 자율 주행에 효율적으로 적용될 수 있는 가능성을 확인하였다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.1
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• pp.88-93
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• 2012

Lane detection methods using camera, which are part of the driver assistance system, have been developed due to the growth of the vehicle technologies. However, lane detection methods are often failed by varying road conditions such as rainy weather and degraded lanes. This paper proposes a method for lane detection which is robust in varying road condition. Lane candidates are extracted by intensity comparison and lane detection filter. Hough transform is applied to compute the lane pair using lane candidates which is straight line in image. Then, a curved lane is calculated by using B-Snake algorithm. Also, weighting value is computed using previous lane detection result to detect the lanes even in varying road conditions such as degraded/missed lanes. Experimental results proved that the proposed method can detect the lane even in challenging road conditions because of weighting process.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.1-11
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• 2021

This paper presents a procedure to extract lane line models using a set of proposed methods. Firstly, an image warping method based on homography is proposed to transform a target image into an image which is efficient to find lane pixels within a certain region in the image. Secondly, a method to use the combination of the results of edge detection and HSL (Hue, Saturation, and Lightness) transform is proposed to detect lane candidate pixels with reliability. Thirdly, erroneous candidate lane pixels are eliminated using a selection area method. Fourthly, a method to fit lane pixels to quadratic polynomials is proposed. In order to test the validity of the proposed procedure, a set of black-box images captured under varying illumination and noise conditions were used. The experimental results show that the proposed procedure could overcome the problems of color-only and edge-only based methods and extract lane pixels and model the lane line geometry effectively within less than 0.6 seconds per frame under a low-cost computing environment.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1123-1124
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• 2023

본 논문은 실시간 차선 인식을 기반으로 한 자율주행 알고리즘을 제안한다. 자율주행 알고리즘은 크게 차선 인식과 의사결정으로 구분된다. 차선 인식 부분에서는 직관적인 판단을 위해 버드 아이 뷰로 영상데이터를 변환하여 안정적 차선 인식을 위하여 차선 영역을 추출하고 노이즈를 제거하는 전처리과정을 거친다. 이렇게 처리된 영상에서 Hough 변환을 통하여 차선을 검출한다. 의사결정 부분에서는 검출된 차선과 현재 위치를 기반으로 진행할 경로를 결정한다.